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ORCIDJustin Emile Gottschlich
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2020 – today
- 2022
[j1]Abdul Wasay, Nesime Tatbul, Justin Gottschlich:
Machine Programming: Turning Data into Programmer Productivity. Proc. VLDB Endow. 15(12): 3754-3757 (2022)- [i21]Urs C. Muff, Celine Lee, Paul Gottschlich, Justin Gottschlich:
MP-CodeCheck: Evolving Logical Expression Code Anomaly Learning with Iterative Self-Supervision. CoRR abs/2204.07225 (2022) - [i20]Shantanu Mandal, Todd A. Anderson, Javier Turek, Justin Gottschlich, Abdullah Muzahid:
Synthesizing Programs with Continuous Optimization. CoRR abs/2211.00828 (2022) - 2021
- [c22]Kory Becker, Justin Gottschlich:
AI programmer: autonomously creating software programs using genetic algorithms. GECCO Companion 2021: 1513-1521 - [c21]Shantanu Mandal, Todd A. Anderson, Javier Turek, Justin Gottschlich, Shengtian Zhou, Abdullah Muzahid:
Learning Fitness Functions for Machine Programming. MLSys 2021 - [c20]Niranjan Hasabnis, Justin Gottschlich
:
ControlFlag: a self-supervised idiosyncratic pattern detection system for software control structures. MAPS@PLDI 2021: 32-42 - [c19]Tharindu Rusira Patabandi, Anand Venkat, Abhishek Kulkarni, Pushkar Ratnalikar, Mary W. Hall, Justin Gottschlich:
Predictive data locality optimization for higher-order tensor computations. MAPS@PLDI 2021: 43-52 - [i19]Celine Lee, Justin Gottschlich, Dan Roth:
Toward Code Generation: A Survey and Lessons from Semantic Parsing. CoRR abs/2105.03317 (2021) - [i18]Alexander Lavin, Hector Zenil, Brooks Paige, David Krakauer, Justin Gottschlich, Tim Mattson, Anima Anandkumar, Sanjay Choudry, Kamil Rocki, Atilim Günes Baydin, Carina Prunkl, Olexandr Isayev, Erik Peterson, Peter L. McMahon, Jakob H. Macke, Kyle Cranmer, Jiaxin Zhang, Haruko M. Wainwright, Adi Hanuka, Manuela Veloso, Samuel Assefa, Stephan Zheng, Avi Pfeffer:
Simulation Intelligence: Towards a New Generation of Scientific Methods. CoRR abs/2112.03235 (2021) - 2020
- [c18]Yizhak Yisrael Elboher, Justin Gottschlich, Guy Katz:
An Abstraction-Based Framework for Neural Network Verification. CAV (1) 2020: 43-65 - [c17]Lujing Cen, Ryan Marcus, Hongzi Mao, Justin Gottschlich, Mohammad Alizadeh, Tim Kraska:
Learned garbage collection. MAPL@PLDI 2020: 38-44 - [i17]Fangke Ye, Shengtian Zhou, Anand Venkat, Ryan Marcus, Paul Petersen, Jesmin Jahan Tithi, Tim Mattson, Tim Kraska, Pradeep Dubey, Vivek Sarkar, Justin Gottschlich:
Context-Aware Parse Trees. CoRR abs/2003.11118 (2020) - [i16]Roshni G. Iyer, Yizhou Sun, Wei Wang, Justin Gottschlich:
Software Language Comprehension using a Program-Derived Semantic Graph. CoRR abs/2004.00768 (2020) - [i15]Lujing Cen, Ryan Marcus, Hongzi Mao, Justin Gottschlich, Mohammad Alizadeh, Tim Kraska:
Learned Garbage Collection. CoRR abs/2004.13301 (2020) - [i14]Fangke Ye, Shengtian Zhou, Anand Venkat, Ryan Marcus, Nesime Tatbul, Jesmin Jahan Tithi, Paul Petersen, Timothy G. Mattson, Tim Kraska, Pradeep Dubey, Vivek Sarkar, Justin Gottschlich:
MISIM: An End-to-End Neural Code Similarity System. CoRR abs/2006.05265 (2020) - [i13]Akhilesh Gupta, Nesime Tatbul, Ryan Marcus, Shengtian Zhou, Insup Lee, Justin Gottschlich:
Class-Weighted Evaluation Metrics for Imbalanced Data Classification. CoRR abs/2010.05995 (2020) - [i12]Niranjan Hasabnis, Justin Gottschlich:
ControlFlag: A Self-supervised Idiosyncratic Pattern Detection System for Software Control Structures. CoRR abs/2011.03616 (2020)
2010 – 2019
- 2019
- [c16]Mejbah Alam, Justin Gottschlich, Nesime Tatbul, Javier S. Turek, Tim Mattson, Abdullah Muzahid:
A Zero-Positive Learning Approach for Diagnosing Software Performance Regressions. NeurIPS 2019: 11623-11635 - [i11]Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Eric S. Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros G. Dimakis, Pradeep Dubey, Charles Elkan, Grigori Fursin, Gregory R. Ganger, Lise Getoor, Phillip B. Gibbons, Garth A. Gibson, Joseph E. Gonzalez, Justin Gottschlich, Song Han, Kim M. Hazelwood, Furong Huang, Martin Jaggi, Kevin G. Jamieson, Michael I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konecný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Gordon Murray, Dimitris S. Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan Randall Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric P. Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar:
SysML: The New Frontier of Machine Learning Systems. CoRR abs/1904.03257 (2019) - [i10]Shantanu Mandal, Todd A. Anderson, Mejbah Alam, Justin Gottschlich, Abdullah Muzahid:
NetSyn: Neural Evolutionary Technique to Synthesize Programs. CoRR abs/1908.08783 (2019) - [i9]Yizhak Yisrael Elboher, Justin Gottschlich, Guy Katz:
An Abstraction-Based Framework for Neural Network Verification. CoRR abs/1910.14574 (2019) - 2018
- [c15]Nesime Tatbul, Tae Jun Lee, Stan Zdonik, Mejbah Alam, Justin Gottschlich:
Precision and Recall for Time Series. NeurIPS 2018: 1924-1934 - [c14]Justin Gottschlich, Armando Solar-Lezama, Nesime Tatbul, Michael Carbin, Martin C. Rinard, Regina Barzilay, Saman P. Amarasinghe, Joshua B. Tenenbaum, Tim Mattson:
The three pillars of machine programming. MAPL@PLDI 2018: 69-80 - [e2]Justin Gottschlich, Alvin Cheung:
Proceedings of the 2nd ACM SIGPLAN International Workshop on Machine Learning and Programming Languages, MAPL@PLDI 2018, Philadelphia, PA, USA, June 18-22, 2018. ACM 2018 [contents] - [i8]Justin Gottschlich:
Paranom: A Parallel Anomaly Dataset Generator. CoRR abs/1801.03164 (2018) - [i7]Tae-Jun Lee, Justin Gottschlich, Nesime Tatbul, Eric Metcalf, Stan Zdonik:
Greenhouse: A Zero-Positive Machine Learning System for Time-Series Anomaly Detection. CoRR abs/1801.03168 (2018) - [i6]Tae Jun Lee, Justin Gottschlich, Nesime Tatbul, Eric Metcalf, Stan Zdonik:
Precision and Recall for Range-Based Anomaly Detection. CoRR abs/1801.03175 (2018) - [i5]Lindsey Kuper, Guy Katz, Justin Gottschlich, Kyle Julian, Clark W. Barrett, Mykel J. Kochenderfer:
Toward Scalable Verification for Safety-Critical Deep Networks. CoRR abs/1801.05950 (2018) - [i4]Nesime Tatbul, Tae Jun Lee, Stan Zdonik, Justin Gottschlich:
A New Model for Evaluating Range-Based Anomaly Detection Algorithms. CoRR abs/1803.03639 (2018) - [i3]Justin Gottschlich, Armando Solar-Lezama, Nesime Tatbul, Michael Carbin, Martin C. Rinard, Regina Barzilay, Saman P. Amarasinghe, Joshua B. Tenenbaum, Tim Mattson:
The Three Pillars of Machine-Based Programming. CoRR abs/1803.07244 (2018) - 2017
- [e1]Tatiana Shpeisman, Justin Gottschlich:
Proceedings of the 1st ACM SIGPLAN International Workshop on Machine Learning and Programming Languages, MAPL@PLDI 2017, Barcelona, Spain, June 18, 2017. ACM 2017, ISBN 978-1-4503-5071-6 [contents] - [i2]Kory Becker, Justin Gottschlich:
AI Programmer: Autonomously Creating Software Programs Using Genetic Algorithms. CoRR abs/1709.05703 (2017) - [i1]Mohammad Mejbah Ul Alam, Justin Gottschlich, Md. Mostofa Ali Patwary, Saeef Ahmad, Abdullah Muzahid:
AutoCon: Regression Testing for Detecting Cache Contention Anomalies Using Autoencoder. CoRR abs/1709.07536 (2017) - 2015
- [c13]Yujie Liu, Justin Gottschlich, Gilles Pokam, Michael F. Spear:
TSXProf: Profiling Hardware Transactions. PACT 2015: 75-86 - 2014
- [c12]Irina Calciu, Justin Gottschlich, Tatiana Shpeisman, Gilles Pokam, Maurice Herlihy:
Invyswell: a hybrid transactional memory for haswell's restricted transactional memory. PACT 2014: 187-200 - [c11]Michael Wong, Eduard Ayguadé, Justin Gottschlich, Victor Luchangco, Bronis R. de Supinski, Barna L. Bihari:
Towards Transactional Memory for OpenMP. IWOMP 2014: 130-145 - 2013
- [c10]Justin Emile Gottschlich, Gilles Pokam, Cristiano Pereira, Youfeng Wu:
Concurrent predicates: A debugging technique for every parallel programmer. PACT 2013: 331-340 - [c9]Irina Calciu, Justin Emile Gottschlich, Maurice Herlihy:
Using Elimination and Delegation to Implement a Scalable NUMA-Friendly Stack. HotPar 2013 - [c8]Justin Emile Gottschlich, Rob C. Knauerhase, Gilles Pokam:
But How Do We Really Debug Transactional Memory Programs? HotPar 2013 - [c7]Gilles Pokam, Klaus Danne, Cristiano Pereira, Rolf Kassa, Tim Kranich, Shiliang Hu, Justin Emile Gottschlich, Nima Honarmand, Nathan Dautenhahn, Samuel T. King, Josep Torrellas:
QuickRec: prototyping an intel architecture extension for record and replay of multithreaded programs. ISCA 2013: 643-654 - 2012
- [c6]Justin Emile Gottschlich, Maurice Herlihy, Gilles Pokam, Jeremy G. Siek:
Visualizing transactional memory. PACT 2012: 159-170 - 2011
- [c5]Justin Emile Gottschlich, JaeWoong Chung:
Optimizing the Concurrent Execution of Locks and Transactions. LCPC 2011: 124-140 - [c4]Gilles Pokam, Cristiano Pereira, Shiliang Hu, Ali-Reza Adl-Tabatabai, Justin Emile Gottschlich, Jungwoo Ha, Youfeng Wu:
CoreRacer: a practical memory race recorder for multicore x86 TSO processors. MICRO 2011: 216-225 - 2010
- [c3]Justin Emile Gottschlich, Manish Vachharajani, Jeremy G. Siek:
An efficient software transactional memory using commit-time invalidation. CGO 2010: 101-110
2000 – 2009
- 2009
- [c2]Justin Emile Gottschlich, Jeremy G. Siek, Manish Vachharajani, Dwight Y. Winkler, Daniel A. Connors:
An efficient lock-aware transactional memory implementation. ICOOOLPS@ECOOP 2009: 10-17 - 2008
- [c1]Justin Emile Gottschlich, Daniel A. Connors:
Optimizing consistency checking for memory-intensive transactions. PODC 2008: 451
Coauthor Index
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